AWS 문서 AWS SDK SDK 예제
기계 번역으로 제공되는 번역입니다. 제공된 번역과 원본 영어의 내용이 상충하는 경우에는 영어 버전이 우선합니다.
Amazon Bedrock에서 여러 파운데이션 모델 간접 호출
다음 코드 예제는 Amazon Bedrock의 다양한 대형 언어 모델 (LLMs) 에 프롬프트를 준비하고 전송하는 방법을 보여줍니다.
- Go
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- SDKGo V2의 경우
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참고
더 많은 내용이 있습니다 GitHub. AWS 코드 예시 리포지토리
에서 전체 예시를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock에서 여러 파운데이션 모델을 간접 호출합니다.
// InvokeModelsScenario demonstrates how to use the Amazon Bedrock Runtime client // to invoke various foundation models for text and image generation // // 1. Generate text with Anthropic Claude 2 // 2. Generate text with AI21 Labs Jurassic-2 // 3. Generate text with Meta Llama 2 Chat // 4. Generate text and asynchronously process the response stream with Anthropic Claude 2 // 5. Generate and image with the Amazon Titan image generation model // 6. Generate text with Amazon Titan Text G1 Express model type InvokeModelsScenario struct { sdkConfig aws.Config invokeModelWrapper actions.InvokeModelWrapper responseStreamWrapper actions.InvokeModelWithResponseStreamWrapper questioner demotools.IQuestioner } // NewInvokeModelsScenario constructs an InvokeModelsScenario instance from a configuration. // It uses the specified config to get a Bedrock Runtime client and create wrappers for the // actions used in the scenario. func NewInvokeModelsScenario(sdkConfig aws.Config, questioner demotools.IQuestioner) InvokeModelsScenario { client := bedrockruntime.NewFromConfig(sdkConfig) return InvokeModelsScenario{ sdkConfig: sdkConfig, invokeModelWrapper: actions.InvokeModelWrapper{BedrockRuntimeClient: client}, responseStreamWrapper: actions.InvokeModelWithResponseStreamWrapper{BedrockRuntimeClient: client}, questioner: questioner, } } // Runs the interactive scenario. func (scenario InvokeModelsScenario) Run() { defer func() { if r := recover(); r != nil { log.Printf("Something went wrong with the demo: %v\n", r) } }() log.Println(strings.Repeat("=", 77)) log.Println("Welcome to the Amazon Bedrock Runtime model invocation demo.") log.Println(strings.Repeat("=", 77)) log.Printf("First, let's invoke a few large-language models using the synchronous client:\n\n") text2textPrompt := "In one paragraph, who are you?" log.Println(strings.Repeat("-", 77)) log.Printf("Invoking Claude with prompt: %v\n", text2textPrompt) scenario.InvokeClaude(text2textPrompt) log.Println(strings.Repeat("-", 77)) log.Printf("Invoking Jurassic-2 with prompt: %v\n", text2textPrompt) scenario.InvokeJurassic2(text2textPrompt) log.Println(strings.Repeat("-", 77)) log.Printf("Invoking Llama2 with prompt: %v\n", text2textPrompt) scenario.InvokeLlama2(text2textPrompt) log.Println(strings.Repeat("=", 77)) log.Printf("Now, let's invoke Claude with the asynchronous client and process the response stream:\n\n") log.Println(strings.Repeat("-", 77)) log.Printf("Invoking Claude with prompt: %v\n", text2textPrompt) scenario.InvokeWithResponseStream(text2textPrompt) log.Println(strings.Repeat("=", 77)) log.Printf("Now, let's create an image with the Amazon Titan image generation model:\n\n") text2ImagePrompt := "stylized picture of a cute old steampunk robot" seed := rand.Int63n(2147483648) log.Println(strings.Repeat("-", 77)) log.Printf("Invoking Amazon Titan with prompt: %v\n", text2ImagePrompt) scenario.InvokeTitanImage(text2ImagePrompt, seed) log.Println(strings.Repeat("-", 77)) log.Printf("Invoking Titan Text Express with prompt: %v\n", text2textPrompt) scenario.InvokeTitanText(text2textPrompt) log.Println(strings.Repeat("=", 77)) log.Println("Thanks for watching!") log.Println(strings.Repeat("=", 77)) } func (scenario InvokeModelsScenario) InvokeClaude(prompt string) { completion, err := scenario.invokeModelWrapper.InvokeClaude(prompt) if err != nil { panic(err) } log.Printf("\nClaude : %v\n", strings.TrimSpace(completion)) } func (scenario InvokeModelsScenario) InvokeJurassic2(prompt string) { completion, err := scenario.invokeModelWrapper.InvokeJurassic2(prompt) if err != nil { panic(err) } log.Printf("\nJurassic-2 : %v\n", strings.TrimSpace(completion)) } func (scenario InvokeModelsScenario) InvokeLlama2(prompt string) { completion, err := scenario.invokeModelWrapper.InvokeLlama2(prompt) if err != nil { panic(err) } log.Printf("\nLlama 2 : %v\n\n", strings.TrimSpace(completion)) } func (scenario InvokeModelsScenario) InvokeWithResponseStream(prompt string) { log.Println("\nClaude with response stream:") _, err := scenario.responseStreamWrapper.InvokeModelWithResponseStream(prompt) if err != nil { panic(err) } log.Println() } func (scenario InvokeModelsScenario) InvokeTitanImage(prompt string, seed int64) { base64ImageData, err := scenario.invokeModelWrapper.InvokeTitanImage(prompt, seed) if err != nil { panic(err) } imagePath := saveImage(base64ImageData, "amazon.titan-image-generator-v1") fmt.Printf("The generated image has been saved to %s\n", imagePath) } func (scenario InvokeModelsScenario) InvokeTitanText(prompt string) { completion, err := scenario.invokeModelWrapper.InvokeTitanText(prompt) if err != nil { panic(err) } log.Printf("\nTitan Text Express : %v\n\n", strings.TrimSpace(completion)) }
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자세한 API 내용은 AWS SDK for Go APIReference의 다음 항목을 참조하십시오.
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- JavaScript
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- SDK JavaScript (v3) 에 대한
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참고
더 많은 정보가 있습니다. GitHub AWS 코드 예시 리포지토리
에서 전체 예시를 찾고 설정 및 실행하는 방법을 배워보세요. import { fileURLToPath } from "url"; import { Scenario, ScenarioAction, ScenarioInput, ScenarioOutput, } from "@aws-doc-sdk-examples/lib/scenario/index.js"; import { FoundationModels } from "../config/foundation_models.js"; /** * @typedef {Object} ModelConfig * @property {Function} module * @property {Function} invoker * @property {string} modelId * @property {string} modelName */ const greeting = new ScenarioOutput( "greeting", "Welcome to the Amazon Bedrock Runtime client demo!", { header: true }, ); const selectModel = new ScenarioInput("model", "First, select a model:", { type: "select", choices: Object.values(FoundationModels).map((model) => ({ name: model.modelName, value: model, })), }); const enterPrompt = new ScenarioInput("prompt", "Now, enter your prompt:", { type: "input", }); const printDetails = new ScenarioOutput( "print details", /** * @param {{ model: ModelConfig, prompt: string }} c */ (c) => console.log(`Invoking ${c.model.modelName} with '${c.prompt}'...`), { slow: false }, ); const invokeModel = new ScenarioAction( "invoke model", /** * @param {{ model: ModelConfig, prompt: string, response: string }} c */ async (c) => { const modelModule = await c.model.module(); const invoker = c.model.invoker(modelModule); c.response = await invoker(c.prompt, c.model.modelId); }, ); const printResponse = new ScenarioOutput( "print response", /** * @param {{ response: string }} c */ (c) => c.response, { slow: false }, ); const scenario = new Scenario("Amazon Bedrock Runtime Demo", [ greeting, selectModel, enterPrompt, printDetails, invokeModel, printResponse, ]); if (process.argv[1] === fileURLToPath(import.meta.url)) { scenario.run(); }
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자세한 API 내용은 AWS SDK for JavaScript APIReference의 다음 항목을 참조하십시오.
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- PHP
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- PHP용 SDK
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참고
자세한 내용은 다음과 같습니다 GitHub. AWS 코드 예시 리포지토리
에서 전체 예시를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon LLMs Bedrock에서 여러 번 호출할 수 있습니다.
namespace BedrockRuntime; class GettingStartedWithBedrockRuntime { protected BedrockRuntimeService $bedrockRuntimeService; public function runExample() { echo "\n"; echo "---------------------------------------------------------------------\n"; echo "Welcome to the Amazon Bedrock Runtime getting started demo using PHP!\n"; echo "---------------------------------------------------------------------\n"; $clientArgs = [ 'region' => 'us-east-1', 'version' => 'latest', 'profile' => 'default', ]; $bedrockRuntimeService = new BedrockRuntimeService($clientArgs); $prompt = 'In one paragraph, who are you?'; echo "\nPrompt: " . $prompt; echo "\n\nAnthropic Claude:"; echo $bedrockRuntimeService->invokeClaude($prompt); echo "\n\nAI21 Labs Jurassic-2: "; echo $bedrockRuntimeService->invokeJurassic2($prompt); echo "\n\nMeta Llama 2 Chat: "; echo $bedrockRuntimeService->invokeLlama2($prompt); echo "\n---------------------------------------------------------------------\n"; $image_prompt = 'stylized picture of a cute old steampunk robot'; echo "\nImage prompt: " . $image_prompt; echo "\n\nStability.ai Stable Diffusion XL:\n"; $diffusionSeed = rand(0, 4294967295); $style_preset = 'photographic'; $base64 = $bedrockRuntimeService->invokeStableDiffusion($image_prompt, $diffusionSeed, $style_preset); $image_path = $this->saveImage($base64, 'stability.stable-diffusion-xl'); echo "The generated images have been saved to $image_path"; echo "\n\nAmazon Titan Image Generation:\n"; $titanSeed = rand(0, 2147483647); $base64 = $bedrockRuntimeService->invokeTitanImage($image_prompt, $titanSeed); $image_path = $this->saveImage($base64, 'amazon.titan-image-generator-v1'); echo "The generated images have been saved to $image_path"; } private function saveImage($base64_image_data, $model_id): string { $output_dir = "output"; if (!file_exists($output_dir)) { mkdir($output_dir); } $i = 1; while (file_exists("$output_dir/$model_id" . '_' . "$i.png")) { $i++; } $image_data = base64_decode($base64_image_data); $file_path = "$output_dir/$model_id" . '_' . "$i.png"; $file = fopen($file_path, 'wb'); fwrite($file, $image_data); fclose($file); return $file_path; } }
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자세한 API 내용은 참조의 다음 주제를 참조하십시오.AWS SDK for PHP API
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Amazon Bedrock 기반 모델과 상호 작용할 수 있는 플레이그라운드 애플리케이션을 생성
Step Functions를 사용하여 제너레이티브 AI 애플리케이션을 오케스트레이션하세요